LRFNet: Learning Light Field Reconstruction via a Large Receptive Field Network
Bibliographic record
Abstract
Densely sampled light fields are powerful tools for applications such as post-capture refocusing and virtual reality, but acquiring such data remains costly and technically demanding. While existing reconstruction methods have shown promise, they often succeed only in small-baseline settings and struggle with larger disparities or real-time efficiency. Depth-based approaches are prone to artifacts due to imperfect depth estimates, while non-depth-based methods lack geometric accuracy, fail in occluded or textureless regions, and are typically computationally intensive. In this work, we provide a more effective disentanglement of spatial, angular, and epipolar representations for light field reconstruction. Through dedicated feature extractors and a residual-in-residual architecture enhanced with channel attention, our framework efficiently captures subpixel details and long-range dependencies while adaptively emphasizing the most informative cues. Rigorous ablation studies further highlight the critical role of epipolar feature interactions—an aspect previously overlooked in the literature. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach consistently surpasses state-of-the-art methods across small-and large-baseline scenarios, delivering higher reconstruction quality while maintaining competitive efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".